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This paper proposes a training-time backdoor defense called Trapping and Removing (TR), which introduces a lightweight shortcut branch as a honeypot to trap backdoor knowledge and then discards it, enhanced by a knowledge decoupling strategy with entropy-based weight assignment.
This paper introduces Knowledge-Geometry Decoupling (KGD), a method for pretrain-then-transfer in streaming recommendation systems. It separates pretrained behavioral knowledge from task-specific geometry, enabling continual model refresh without interference, and reports 4-12% improvements over baselines plus successful deployment at Shopee.